Traffic signal control is
a system for synchronizing the timing of any number of traffic signals
in an area, with the aim of reducing stops and overall vehicle delay
or maximizing throughput. Traffic signal control varies in complexity,
from simple systems that use historical data to set fixed timing plans,
to adaptive signal control, which optimizes timing plans for a network
of signals according to traffic conditions in real-time.

Traffic signal improvements
generally provide the greatest payoff for reducing surface street
congestion when compared with other methods, such as widening roads
[12]. Advanced traffic signal control can help ease congestion and
its negative externalities without the cost and environmental impact
of road expansion.

Traffic signal operation can
be described in terms of cycle length, signal phases, and offsets.
A traffic signals phasing plan defines how the signal operates.
Phasing plans can be simple, two-phase plans (one phase per approach)
or can be tailored to allow protected/permitted movements and lead/lag
phases. An intersection with heavy left turning traffic and heavy
opposing through movements would probably include a protected left
turn phase either before the opposing traffic is released (lead phase)
or after the opposing traffic is stopped (lag phase). The cycle length
is the total time required for a complete sequence of signal phases
and is typically between 60 to 120 seconds for a four-legged intersection.
The offset between successive traffic signals is the time difference
between the start of the green phase at an upstream intersection as
related to the start of the green phase at an adjacent downstream
intersection. See our Telecommunications Diagrams on Adaptive
Signal Control and Fixed
Signal Control for more information.

Traffic signals may operate
independently, or as a system. The scope of control can be grouped
in 3 categories:

ˇIndividual Intersection Control
 A single traffic signal operates in a pre-timed, actuated,
or traffic responsive mode, without affecting the operation of other
traffic signals.

ˇArterialControl 
Two or more traffic signals operate synchronously along an arterial
street in a pre-timed progression, traffic responsive, or adaptive
control mode.

ˇNetwork Control  Traffic
Signals throughout an entire network of intersections are coordinated
through a timing plan created offline, or an adaptive control strategy.

Modes
of Operation

There are many different levels
of traffic signal control, from the individual intersection with pre-timed
control to the network-wide system with adaptive control. Here are
descriptions of the different modes of operation, from the simplest
to the most complex:

1.Pre-timed- Under pre-timed operation, the master
controller sets signal phases and the cycle length based on predetermined
rates. These rates are determined from historical data. Pre-timed
signal control is appropriate for areas where traffic demand is very
predictable.

2. Progression Schemes - A progression scheme is a simple
way of coordinating signals along an arterial, which is common in
many urban areas. The signals can be set manually to run in a constant,
synchronous manner. There are 3 different types of progression schemes:

Simultaneous - Under
simultaneous progression, all signals along the route operate with
the same cycle length and display green at the same time.
All traffic moves at once and a short time later all traffic stops
at the nearest intersection to allow cross street traffic to move.
This type of progression is typically used in downtown areas where
intersections are close together, 300 to 500 ft, and uniformly spaced.

Alternate - For alternate progression, there
is a common cycle length, however each successive signal or group
of signals shows opposite indications. This type of progression
is associated with uniform spacing of intersections. Ideal
spacing is in the range of 1000 to 2,000 feet.

Limited or simple - Limited/simple progression
schemes employ a common cycle length, though the relationship of
the indications between intersections vary because spacing between
intersections is not uniform, and therefore offsets at each intersection
differ. This type of progression scheme is typically used
where traffic flow is uniform throughout the day.

Flexible - Flexible progression schemes are
identical to simple progression schemes, except that the common
cycle length can be changed to reflect changing traffic patterns.
Similar to limited or simple progression schemes, flexible progression
schemes use different offsets between intersections.

3. Actuated - An actuated controller operates
based on traffic demands as registered by the actuation of vehicle
and/or pedestrian detectors. There are several types of actuated
controllers, but their main feature is the ability to adjust the
signals pre-timed phase lengths in response to traffic flow.
If there are no vehicles detected on an approach, the controller can
skip that phase. The green time for each approach is a function of
the traffic flow, and can be varied between minimum and maximum lengths
depending on flows. Cycle lengths and phases are adjusted at intervals
set by vehicle actuation of pavement loops.

ˇ
Semi-Actuated Control- A semi-actuated
controller provides for traffic actuation of all phases except the
main phase. A continuous green is maintained on the major
street except when a demand is registered by the minor street detector.
The right of way always returns to the major street when no vehicles
are present on the minor street or a timing limit has been reached.
Semi-actuated operation is best suited for locations with low volume
minor street traffic. It may also be used to permit pedestrian
crossings at mid street.

ˇFull Actuated Control-
Under full actuated control, the function of the controller is to
measure traffic flow on all approaches to an intersection and make
assignments of the right of way in accordance with traffic demand.
Full actuated control requires placement of detectors on all approaches
to the intersection. The controllers ability to respond
to traffic flow provides for maximum efficiency at individual locations.
This type of control is appropriate for intersections where the
demand proportions from each leg of the intersection are less predictable.

4. Traffic Responsive -
In traffic responsive mode, signals receive inputs that reflect current
traffic conditions, and use this data to choose an appropriate timing
plan from a library of different plans. An individual signal or a
network of several signals may be traffic responsive. Capabilities
include:

ˇVehicle Actuated - uses
data from presence detectors and modifies the phase splits based
on vehicle actuation and gaps. This procedure addresses current
traffic and does not require traffic projections.

ˇFuture traffic prediction
- control system uses the volume data from system detectors
and projects future conditions.

ˇPattern Matching - the
volume and occupancy data from system detectors are smoothed and
weighted and compared with profiles in memory. This enables
identification of the stored profile most closely matching the existing
traffic conditions. When a pattern is identified, appropriate
parameters are placed into operation.

5. Adaptive Control Strategies (ACS) - these
systems are currently the most advanced and complex control systems
available. They are similar to traffic responsive signals in that
they receive real-time data through detectors, but instead of matching
current conditions to an existing timing plan, the system uses an
online computer to create an optimal timing plan. No library of timing
plans is needed, which works well for areas with high rates of growth,
where libraries of timing plans would need to be updated frequently.

Offline
Signal Timing Optimization Models

As computer technology has improved,
computer models have replaced manual setting and optimization of signal
timing plans. These powerful models use historical data and computer
simulation to create an optimal signal timing plan that either maximizes
bandwidth or minimizes total delay. The basic ingredients of these
models include (a) a traffic flow model, and (b) an algorithm for
optimizing a specified performance criterion. The following
are examples of signal timing optimization programs that are available
either in the public domain, or from private companies [3, 4]:

ˇUrban Traffic Control Systems
(UTCS)-UTCS is a centralized traffic control system that
controls all intersections in a system with fixed or variable timing
plans. UTCS was developed by the Federal Highway Administration
in the 1970's as part of a research project that sought to develop
and test a variety of advanced network control concepts and strategies.
Historical data based on time of day and day of week are often the
basis of the plan. Some UTCS provide critical intersection control
(CIC), a feature that allows vehicle actuated adjustments of green
time splits at selected signals. The control strategies in the UTCS
project are categorized into three generations; the first generation
is an offline optimization tool, described below, and the other generations
are online tools, which will be discussed in the next section.

First Generation Control (1-GC) - 1-GC control
uses pre stored signal timing plans that are calculated off-line
based on historic traffic data. The timing plan can be selected
on the basis of time of day, by direct operator selection, or by
matching from an existing library a plan best suited to recently
measured traffic conditions in traffic responsive mode. Under
traffic responsive mode, the software updates the plan every 15
minutes with a smooth transition between regimes. 1-GC has the CIC
feature described above.

ˇTraffic Network Study Tool
(TRANSYT) -TRANSYT is one of
the most widely used signal timing programs. The original version
of TRANSYT was developed by the Transportation and Road Research Laboratory
in England in 1968. Though TRANSYT is most commonly used as
an offline optimization tool, it may also be used in an online fashion
to compute signal settings every few minutes and download these settings
to the field. TRANSYT is a macroscopic, deterministic simulation
and optimization model. The model requires the link flows and
link turning proportions as inputs and assumes them to be constant
for the entire simulation period. The program optimizes splits
and offsets given a set cycle length and carries out a series of iterations
between its traffic simulation module and the signal setting optimization
module.

A version tailored specifically
for the United States was created, entitled TRANSYT-7F. The TRANSYT-7F
program is capable of evaluating a coordinated network or arterial
of up to 50 intersections with up to 250 directional links.

ˇMAXBAND - MAXBAND is a bandwidth
optimization program that calculates signal timing plans on arterials
and triangular networks. MAXBAND produces cycle lengths, offsets,
speeds, and phased sequences to maximize a weighted sum of bandwidths.
The primary advantage of MAXBAND is the freedom to provide a range
for the cycle time and speed. The lack of incorporated bus flows
and limited field tests are disadvantages of MAXBAND.

ˇPASSER II-80 - PASSER II-80
is a bandwidth optimization program that calculates signal timing
plans on linear arterials. A modified version of Webster's delay
equation is used to approximate platoon effects. Outputs
include cycle length, phase sequencing, splits, offsets, and band
speed that maximize bandwidth in both directions. Advantages
are flexibility to vary cycle length and bandwidth and consideration
of multiphase operation under a variety of timing strategies.
Disadvantages include lack of emissions or fuel consumption data.

ˇPASSER III - PASSER III
computes cycle length, phase sequencing, and splits that minimize
average delay per vehicle for a pre-timed interchange. PASSER
III uses a deterministic, macroscopic time-scan optimization model.
It can also determine splits and offsets for interchange signals along
a frontage road, but in this case bandwidth is the performance objective.

ˇSIGOP - By using a macroscopic
traffic flow model, SIGOP determines cycle length, splits, and offsets
of signals in a grid network that minimize delay. SIGOP can
handle up to 150 intersections. Outputs include time-space plots
along selected arterials and link statistics. Up to four phases
can be modeled in SIGOP.

ˇMOTION- MOTION (Method
for the Optimization of Traffic Signals in Online controlled Networks)
is a prototype system for the automatic control of traffic lights
under the global goal of optimized flow conditions and waiting times
in a network. The first field implementation took place in Cologne,
Germany in 1995 and its basic methodology was developed in the ATT/DRIVE
II project. The basic idea is to combine the advantages of well-designed
'Green Waves' for major traffic streams in a network with the flexibility
of an immediate response of local signals to the actual state of traffic.
MOTION determines a network cycle time, mainly according to the traffic
volumes at critical intersections. Based on the current average
turning movements at intersections, a number of alternative basic
signal programs are then calculated. In the second step the
O-D pattern and corresponding traffic streams through the network
are determined. They create, with external preconditions, the
network optimization plan. Another feature of the system is
that it gives special priority to public transportation vehicles.

Adaptive
Control Strategies

As opposed to the models outlined
above, which use historical data to create one or more optimized timing
plans, adaptive control strategies use real time data from detectors
to perform constant optimizations on the signal timing plan for an
arterial or a network. This means that signals can adapt to non-recurring
congestion, incidents, events, or traffic demand growth over time,
without needing to be reset.

ˇUTCS Control Strategies
 As mentioned above, the second and third generation
control strategies developed by the FHWA are adaptive control strategies:

Second Generation Control (2-GC) -
2-GC control uses an online strategy that implements signal timing
plans based on real time surveillance data and predicted values.
The optimization process can be repeated every five minutes. However,
to avoid transition disturbances, new timing plans cannot be implemented
more than once every 10 minutes. The software also contains
a traffic prediction model, CIC, and a transition model to minimize
transition time between two plans.

Third Generation Control (3-GC)  Similar
to 2-GC, 3-GC is a fully responsive, online traffic control system.
Similar to 2-GC, it computes control plans to minimize a network
wide objective using predicted traffic conditions. It differs
from the 2-GC model in that the period after which timing plans
are revised is shortened to 3 to 5 minutes, and the cycle lengths
are allowed to vary among the signals during the control period.

Table 1 Comparison
of UTCS Control Strategies

FEATURE

First Generation Control
(1-GC)

Second Generation Control
(2-GC)

Third Generation control
(3-GC)

Update interval

15 min

5-10 min

3-5 min

Control plan generation

Off line optimization
selection from a library by time of day, traffic responsive,
or manual mode.

Online optimization

Online optimization

Traffic prediction

None

Historically based

Smoothed current values

Cycle length determination

Fixed within each section

Fixed within variable
groups of intersections

Variable in time and space.
Predetermined for control period.

Source: Gartner, Nathan,
Chronis Stamatindius, and Phillip Tarnoff. Development of Advanced
Traffic Signal Control Strategies for ITS. Transportation Research
Record 1494, 1996.

ˇDistributed Intelligence Traffic
Control System (DITCS) - DITCS is a control system in which
intersection controllers use timing plans but can dynamically adjust
the splits to suit traffic conditions at the controller level.
DITCS are closed loop systems providing real-time traffic adaptive
control. The central system sends synchronization pulses, but most
functions are performed at the intersection level maximizing the use
of computing power. Some well known DITCS are Sydney Coordinated
Traffic Adaptive System (SCATS) and TracoNet, described below:

ˇSCATS - Developed by the
New South Wales Department of Main Roads, SCATS is a dynamic control
system with a decentralized architecture. SCATS updates intersection
cycle length using the detectors at the stop line. SCATS allows for
phase skipping. Offsets between adjacent intersections are predetermined
and adjusted with the cycle time and progression speed factors.

ˇTracoNet- TracoNet is a
distributed intelligence closed loop network control system used for
coordinating, controlling and facilitating the flow of vehicular traffic.
It can operate in all control modes, including fully
actuated. Traffic responsive algorithms based on pattern matching
are also available.

ˇSplit Cycle and Offset Optimization
Technique (SCOOT)- SCOOT is an off-the-shelf centralized
computerized traffic control model developed at the Transportation
Road Research Laboratory in the U.K. It is an enhancement over
first generation UTCS systems and provides real-time adaptive control.
SCOOT uses system detectors to measure traffic flow profiles in real
time, and along with predetermined travel times and the degree of
saturation (the ratio of flow-to-capacity), predicts queues at intersections.
Adjustments of cycle length, phase splits and offsets are made in
small steps to operate at a preset degree of saturation (usually 90%).
Tests have shown that SCOOT is most effective when demand approaches,
but is less than, capacity, where demand is unpredictable, and when
distances between intersections are short. Traffic control systems
using SCOOT are prevalent in Australia, Asia, and recently in North
America. The three key principles of the SCOOT system that make
it different from the TRANSYT model are:

ˇit measures the cyclic flow profile
in real time as opposed to deriving it from upstream turning movements

ˇit updates an online model of
queues continuously as opposed to only updating once

ˇit makes incremental as opposed
to global optimizations to the signal settings

The SCOOT and SCATS traffic models are built on
a vertical queue model and thus can not consider the effect of downstream
link congestion on the signal output. These models operate
fairly well as long as the network is not overly congested.
However, they fail to model the effect of downstream congestion
on the capacity of upstream intersections during queue spillback.
The queuing model is updated from queue measurements from the field.

ˇ
Real-time Traffic Adaptive Signal Control
System (RT-TRACS) - In 1991 the FHWA solicited proposals for the
development of a real-time, traffic adaptive signal control system
called RT-TRACS. Shortly thereafter, the FHWA contracted with
PB Farradyne to develop and implement RT-TRACS. The RT-TRACS control
logic assesses the current status of the network with forecasting
capabilities, allowing proactive, not reactive, response. The
most fundamental requirement of this system is to effectively manage
and respond to rapid variations in traffic conditions. RT-TRACS
consists of a number of real-time control prototypes that each function
optimally under different traffic and geometric conditions. When conditions
dictate, RT-TRACS can automatically switch to another strategy. The
FHWA realizes that this control logic must be integrated with freeway
performance data and provide network wide control. A thorough understanding
of past experience with advanced traffic signal control strategies
is critical to the development of effective RT-TRACS strategies for
ITS. Features of the RT-TRACS design include:

ˇimproved fallback capabilities
in case of surveillance system failure;

ˇeffective use of the accumulated
experience with real-time control.

Five prototypes strategies
are currently being developed and evaluated for use in the RT- TRACS
program. The FHWA awarded five separate contracts to develop these
real-time prototype strategies. The contracts were awarded to the
University of Arizona, the University of Minnesota, the University
of Massachusetts (Lowell)/ PB Farradyne, Wright State University in
Ohio, and the University of Maryland/University of Pittsburgh. Kaman
Sciences Corporation is responsible for evaluating these prototypes
using the CORSIM simulation model. In late 1997, the FHWA and
the University of Arizona teamed to develop and field test one of
these prototypes, RHODES, an open architecture version of RT-­TRACS that will utilize an alternative database
management system and NTCIP protocol.

Three of these prototypes,
the RHODES prototype from the University of Arizona, OPAC
(Optimization Policies for Adaptive Control) from PB Farradyne/ University
of Massachusetts (Lowell), and RTACL from the University of
Pittsburgh/University of Maryland, are at an advanced state of development.
Initial simulation testing showed that these prototype strategies
produced statistically significantimprovements
in traffic throughput and reduced average delay. The results of the
laboratory evaluation of the RHODES prototype have indicated a reduction
in delay, stops, and fuel consumption of 24 percent, 9 percent, and
6 percent, respectively, while maintaining the same throughput as
the baseline case (vehicle actuated control). A 16-intersection arterial
in Reston, Virginia has been selected for the field implementation.
Instrumentation of the arterial is in progress. Further testing is
expected to occur in Seattle, Washington, and Chicago, Illinois.

ATSAC (Automated Traffic
Surveillance and Control) -- The city of Los Angeles created ATSAC
--based originally on UTCS-- one of the earliest and most extensive
advanced traffic management systems, including centralized, adaptive
traffic signal control. The system includes surveillance via loop
detectors and closed circuit television, signal optimization software,
and real-time remote control of signals.[14] Please see the case
study below.

Standard traffic controllers
are the field hardware used in signalized intersection control. It
is important to consider the capability of existing traffic controllers
when implementing a new traffic signal control strategy, as earlier
models may not be able to process the amount of data required.

NEMA TS-1- The first broadly accepted
industry defined traffic controller, NEMA TS-1 is based on
conformance to standard mechanical and electrical connectors.
The architecture is closed to the customer, which does not
allow a DOT to change the software / hardware and functionality
of the product.

Caltrans 170- Created by Caltrans in
the 1970s, this controller defined not only the interface
standard, but also the microprocessor to be used and its memory
map. This approach allowed independent software developers
to create products, and DOTs benefited from multiple hardware
and software vendors. However, they are now becoming outdated
because they are not able to support today's standards.

NEMA TS-2- Recognizing the need for
a new controller, NEMA published the TS-2 specification in
1992. The system utilizes a serial I/O architecture
to provide modularity and expandability for the I/O detectors.
The TS-2 architecture remains closed to the integrator, so
the inherent limitations of a closed system remain.
Although the specifications have been out since 1992, many
believe that the Caltrans 2070 specification will preclude
widespread adoption of NEMA TS-2.

Caltrans 2070- The Caltrans Model 2070
ATC traffic controller is a new and advanced controller intended
to satisfy the high-end needs of the advanced freeway and urban
control systems and those applications requiring greater performance
and/or flexibility than is currently available with the Model
170/E traffic controllers. Specifications for the Model 2070
controller are currently being refined.

Traffic signal control can provide
significant benefits for traffic flow on a surface street network.
However, it seems that the most advanced systems are not always the
most effective. Careful attention must be paid to implement a traffic
signal control system that is appropriate and cost-effective for the
area. In addition, it is important to assess the current state of
the existing traffic signal control system when projecting results
of an improvement to the system. If a system is currently pretimed,
and ACS is installed, there will probably be a significant improvement.
However, if the current system is already fairly updated, the improvements
will generally not be as great.

Surprisingly, extensive field
tests in the 1980s, which compared each generation of UTCS on an arterial
and a grid network to a standard, pre-timed system, showed that the
simpler methods performed better on average (See Table 3). 1-GC, in
its various modes of operation, performed best overall, and demonstrated
that it can provide measurable reductions in total travel time over
that which could be attained with a well maintained fixed time system.
In 2-GC and 3-GC, the effectiveness of the control system response
depends entirely on the quality of the prediction model. The
traffic responsive plan selection method was generally better than
the time of day method. The results of the 2-GC method were
mixed, but overall inferior to the 1-GC. The 3-GC strategy was
unsuccessful in responding to traffic flows and degraded performance
under almost all traffic conditions. Counter-intuitively, the
more responsive strategies resulted in poorer performance than fixed
cycle, non-responsive strategies. A close examination of the
experiments reveals that expectations were not fulfilled because the
models and procedures used in the UTCS study failed.
Proposed reasons for the limited success of adaptive control included:

ˇInherent inaccuracies in the measurement
prediction cycle, such that the strategies could not respond fast
enough.

Table 3 presents a summary of
some extensive field tests conducted on UTCS control systems in the
United States in the early 1980's, comparing each generation on an
arterial and grid network. The UTCS strategies are compared to operation
with standard pre-timed traffic control. (+ indicates an increase
in the travel time)

Table 3 Comparison of
Results of UTCS Strategies

% Change in aggregate
veh-minutes of travel with respect to base

UTCS Strategy

AM Peak

Off Peak

PM Peak

Daily Average

1-GC (Arterial)

-2.6

-4.0

-12.2

NA

1-GC (Network)

-3.2

+1.9

-1.6

NA

2-GC (Arterial)

-1.3

-3.8

+0.5

-2.1

2-GC (Network)

+4.4

+1.9

+10.7

+5.2

3-GC (Arterial)

+9.2

+24.0

+21

+16.9

3-GC (Network)

+14.1

-0.5

+7.0

+8.2

Source: Gartner, Nathan,
Chronis Stamatindius, and Phillip Tarnoff. Development of Advanced
Traffic Signal Control Strategies for ITS. Transportation Research
Record 1494, 1996.
NA: Data not available

Benefits

Traffic signal control improvements
are very effective at reducing congestion. In fact, they generally
provide the greatest payoff compared with any other method for reducing
congestion on surface streets. [12] Traffic signals do not need to
become state-of-the-art in order to realize great improvements
in traffic flow. Often, one simple improvement, such as interconnecting
signals that were previously operating independently, can produce
significant results. According to [12], projects in the United States
have found that:

Interconnecting previously uncoordinated signals
or pretimed signals, and providing newly optimized timing plans
and a central master control system can result in a travel time
reduction of 10-20 percent.

Installing advanced computer control has resulted
in about a 20 percent travel time reduction when compared to interconnected
pretimed signals using old timing plans.

Installing advanced computer control has resulted
in a 10-16 percent travel time reduction when compared to non-interconnected,
traffic actuated controls.

Installing advanced computer control, when compared
to interconnected pre-timed control with relatively active signal
timing management, has resulted in an 8-10 percent travel time
reduction.

Optimizing traffic signal timing plans, when compared
to previously interconnected signals with various master control
forms and varying previous signal timing qualities, has resulted
in a 10-15 percent reduction in travel time.

In addition to significantly
reducing travel time, traffic signal control improvements also reduce
stops, fuel consumption, and emissions. For example, the Texas Traffic
Light Synchronization Grant Program II (TLS II) achieved reduced fuel
consumption, delay and stops by 13.5 % (20.8 million gallons/year),
29.6% (22 million hours/year), and 11.5% (729 million stops/year),
respectively. The total savings to the public in the form of
reduced fuel, delay, and stops was approximately $252 million in the
following year alone. More significantly, however, the study
indicated that an average of 10 gallons of fuel was saved for every
dollar that was spent on the retiming project [8].

An aggressive signal retiming
effort in California resulted in a benefit-cost ratio of 58 to 1.
The program improved 3,172 signals across the state, resulting in
a 15% reduction in delay, 16% reduction in stops, and a 7.2% reduction
in travel time throughout the system. The money saved from reduced
fuel consumption (8.6%) alone returned the total cost of the program
18 times over. [12]

Adaptive Control Strategies
(ACS) have additional benefits, such as increased safety. ACS can
reduce the number of stops through improved signal coordination, which
in turn reduces the chance of rear-end collisions. In comparison
to fully optimized fixed-time systems, SCATS has been shown to reduce
stops by up to 40 percent [11]. Since implementing SCATS, Broward
County, Florida has seen a 28 percent decrease in stops, and Oakland
County, Michigan showed a 33 percent reduction in stops. ATSAC in
Los Angeles has reduced stops by 41 percent [11].

In
addition, ACS have the added advantage of being able to grow with
a community. The ITS deployment tracking database shows that few areas
re-time their signals each year. In fact, ITE estimates that nearly
75 percent of all signals in the United States need to be re-timed
[11]. Most metropolitan areas do not have the resources to re-time
their signals regularly. However, with ACS there is no need to reset
signals, because the system continually generates new timing plans.
This is especially beneficial to areas of high growth, where even
the best fixed timing plans quickly become out-of-date.

Source:
What Have We Learned about Intelligent Transportation Systems?,
2000

*per
intersection **requires regional hardware

In addition to the initial
cost, signal operations and maintenance costs can be significant,
and must be considered carefully. Several categories of maintenance
should be considered [12]:

Preventative Maintenance, to be performed
at regular intervals to avoid unnecessary problems

Response Maintenance, which
includes quick response to emergency situations as well as trouble-shooting

Design Modification, which
deals with the need to monitor new equipment and new signal locations
in order to ensure safe and effective operation

ACS, when compared to standard
traffic control devices, can reduce operations and maintenance costs,
since the cost of maintenance for an ACS system is much lower than
the cost of retiming. However, it is not that simple, because while
signal retiming costs decrease, other costs, such as loop maintenance
increase. [11]

and Maintenance Costs
for SCOOT Compared to Standard Traffic Control Devices

Equipment/Task

Costs of SCOOT vs. Standard

Controllers

Same

Detectors

Increases

Signal
Plans/Updates

Decreases

Source:
What have we learned about Intelligent Transportation Systems? 2000

Implementation
Challenges

The most common challenge to implementation of traffic
signal control improvements is initial financial cost. Luckily, as
has been seen in California and Texas, the benefits from a well-designed
improvement program far outweigh the initial cost.

It is crucial to use pilot studies and other evaluation
techniques in selecting a system that will work well for a particular
area. Some systems may not improve congestion in a certain area at
all. For example, a limited SCOOT installation in Anaheim,
California, produced little improvement, and even increased
delay in some cases. According to a US Department of Transportation-sponsored
evaluation of the system, detector placement may have been the cause
of the sub-optimal performance. [11]. In addition, in areas with
fairly predictable traffic demand and low growth, a well-maintained
fixed-time/time-of-day signal may perform just as well as ACS.

The increased complexity of new traffic signal control
systems may also be an impediment. Additional training is normally
required for ACS systems, which are not considered user-friendly.
Furthermore, ACS is highly dependent on the communications network
and the traffic detectors. The system cannot work efficiently without
these reliable inputs.

WHERE
IS TRAFFIC SIGNAL CONTROL IMPLEMENTED?

Case
StudiesLos
Angeles, CA

Automated Traffic Surveillance and Control (ATSAC)With its large population and its
auto-dependent urban form, Los Angeles experiences extremely heavy
congestion on its arterials. The situation is exacerbated by activity
centers, such as the coliseum and the airport, which create large
and less predictable surges of traffic. In response to this problem,
the city of Los Angeles created ATSAC, one of the earliest and most
extensive advanced traffic management systems, including centralized,
adaptive traffic signal control. The system, first utilized around
the Coliseum for the 1984 Olympic Games, was initially based on the
FHWAs UTCS signal control software, and customized by a consulting
firm, JHK & Associates. The system includes surveillance via
loop detectors and closed circuit television, signal optimization
software, and real-time remote control of signals.[14]

ATSAC has had tremendous success
in reducing system-wide congestion, as well as in clearing event traffic.
Since the system was implemented, coliseum traffic clears within an
hour after a big concert, compared with over two hours previously.
[14] In addition, the system has been found to reduce stops by 35%,
intersection delay by 20%, travel time by 13%, fuel consumption by
12.5%, and air emissions by 10%. The benefit/cost ratio was found
to be 9.8:1, and the system paid for itself in less than one year.
[15]

Oakland
County, Michigan

Faster and Safer Travel
through Traffic Routing and Advanced Controls (Fast-Trac)About ten years ago, residential population
and economic activity skyrocketed in Oakland County, Michigan. Unfortunately,
along with the benefits of this growth came significant traffic congestion.
The cost of solving the congestion problem was estimated to be almost
$1 billion. Instead of resorting to road and highway expansions,
Oakland County looked for a more innovative approach. The result
was the Faster and Safer Travel through Traffic Routing and Advanced
Controls (Fast-Trac) system.

Fast-Trac
integrates advanced traffic management with advanced traveler information
systems, with the SCATS adaptive control strategy at the core. With
their SCATS system, Oakland County can claim many firsts:
the first adaptive traffic control system in the U.S., the first SCATS
application in the western hemisphere, and the first to use video
image processing with SCATS. Oakland County chose to use video surveillance
instead of loop detectors with SCATS for several reasons. Video cameras
can be installed on any surface and in any weather conditions-- a
very important advantage in the Michigan climate. Also, one video
camera can monitor several lanes of traffic, while a conventional
loop detector can only monitor one.

Fast-Trac
has been very successful on several fronts. There has been an 89-percent
drop in the number of accidents at the most dangerous intersections,
a 100 percent decrease in the number of serious injuries at those
same intersections, and 40-plus hours a year trimmed from the average
commute time.[16]

At
the World Cup soccer matches held in Detroit's Silverdome--and since
then, at other major concerts and special events--tests showed that
the traffic management system eased traffic flow and reduced the need
for police to manually direct traffic. Overall, the program is responsible
for a 19 percent increase in rush-hour travel speed and a significant
decrease in accidents. Studies suggest that Fast-Trac could potentially
reduce the average number of vehicle stops by one-third, decreasing
the incidence of rear-end collisions and reducing carbon monoxide
emissions by 12 percent. [17]

Anaheim,
California

Field Operational Test with SCOOTAnaheim, best known for Disneyland, also
houses many other large event centers, including a convention center;
a professional baseball stadium, Anaheim Stadium; and a professional
hockey ice rink, Arrowhead Pond. These event centers have a collective
maximum attendance of 200,000 people; when combined with Anaheims
300,000 residents, major traffic congestion results. With these unpredictable
surges of event traffic, it seemed that Anaheim was a perfect candidate
for an ACS implementation.

As part of the federally funded
Anaheim Advanced Traffic Control System Field Operations Test (FOT),
aversion 3.1 SCOOT system was installed by Siemens
for a portion of the City of Anaheim network near Arrowhead Pond and
Anaheim Stadium. From fall 1994 to spring 1998, PATH researchers
conducted a study comparing SCOOT to the previous UTCS system, which
was already considered state-of-the-art.

Contrary to expectations,
SCOOT was not found to be an improvement over the UTCS system. The
SCOOT system produced lower intersection delays in some cases, but
more often it produced higher delays. In cases where there was improvement,
the improvement was less than 5%, and in cases where conditions worsened,
the increase in delay was less than 10%.

There were several problems
that led to SCOOT's less-than-ideal performance. Among others, SCOOT
predicts traffic conditions using input from loop detectors located
upstream of the intersection. The loop detectors in Anaheim were located
closer than usual to the intersection, and therefore did not give
SCOOT completely accurate information on current traffic conditions.
Also, as a result of cumulative communication or other system faults,
the SCOOT intersections were unexpectedly being isolated from SCOOT
control. Such faults can be cleared manually in most cases, but this
requires active intervention on the part of the TMC operator. If faults
are actively cleared rather than being permitted to accumulate, the
signals involved usually remain under SCOOT control. The number of
signals slipping from SCOOT control decreased substantially once the
evaluation team demonstrated to Anaheim TMC operators the need to
clear faults as they occurred. Unfortunately, however, these conditions
still resulted in substantial data loss for this portion of the evaluation.[18]

SCOOT's performance in Anaheim
should not be taken as a failure on the part of the control strategy
itself, but rather as a caution to potential ASC implementors, highlighting
the importance of field tests and other preliminary research.